CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients.

CT-based abdominal body composition measures have shown associations with important health outcomes. Artificial intelligence (AI) advances now allow deployment of tools that measure body composition in large patient populations. To assess associations of age, sex, and common systemic diseases on CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample.

BAE-ViT: An Efficient Multimodal Vision Transformer for Bone Age Estimation.

This research introduces BAE-ViT, a specialized vision transformer model developed for bone age estimation (BAE). This model is designed to efficiently merge image and sex data, a capability not present in traditional convolutional neural networks (CNNs). BAE-ViT employs a novel data fusion method to facilitate detailed interactions between visual and non-visual data by tokenizing non-visual information and concatenating all tokens (visual or non-visual) as the input to the model.

Comparison of sequential multi-detector CT and cone-beam CT perfusion maps in 39 subjects with anterior circulation acute ischemic stroke due to a large vessel occlusion.

Purpose: The critical time between stroke onset and treatment was targeted for reduction by integrating physiological imaging into the angiography suite, potentially improving clinical outcomes. The evaluation was conducted to compare C-Arm cone beam CT perfusion (CBCTP) with multi-detector CT perfusion (MDCTP) in patients with acute ischemic stroke (AIS).

Approach: Thirty-nine patients with anterior circulation AIS underwent both MDCTP and CBCTP.

Correlation of HbA1c levels with CT-based body composition biomarkers in diabetes mellitus and metabolic syndrome.

Diabetes mellitus and metabolic syndrome are closely linked with visceral body composition, but clinical assessment is limited to external measurements and laboratory values including hemoglobin A1c (HbA1c). Modern deep learning and AI algorithms allow automated extraction of biomarkers for organ size, density, and body composition from routine computed tomography (CT) exams. Comparing visceral CT biomarkers across groups with differing glycemic control revealed significant, progressive CT biomarker changes with increasing HbA1c.

Intrapatient Changes in CT-Based Body Composition After Initiation of Semaglutide (Glucagon-Like Peptide-1 Receptor Agonist) Therapy.

The long-acting glucagon-like peptide-1 receptor agonist semaglutide is used to treat type 2 diabetes or obesity in adults. Clinical trials have observed associations of semaglutide with weight loss, improved control of diabetes, and cardiovascular risk reduction. The purpose of this study was to evaluate intrapatient changes in body composition after initiation of semaglutide therapy by applying an automated suite of CT-based artificial intelligence (AI) body composition tools.

AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults.

Objectives: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population.

Methods: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes.

Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis.

Objective: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values.

Materials And Methods: A total of 1676 adults (mean age, 68.4 ± 10.

Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.

Objective: Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

Purpose: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.

Methods: In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.

Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation.

Purpose: To assess the ability of an automated AI tool to detect intravenous contrast material (IVCM) in abdominal CT examinations using spleen attenuation.

Methods: A previously validated automated AI tool measuring the attenuation of the spleen was deployed on a sample of 32,994 adult (age ≥ 18) patients (mean age, 61.9 ± 14.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds.

Opportunistic Screening: Scientific Expert Panel.

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability.

Technical Adequacy of Fully Automated Artificial Intelligence Body Composition Tools: Assessment in a Heterogeneous Sample of External CT Examinations.

Clinically usable artificial intelligence (AI) tools analyzing imaging studies should be robust to expected variations in study parameters. The purposes of this study were to assess the technical adequacy of a set of automated AI abdominal CT body composition tools in a heterogeneous sample of external CT examinations performed outside of the authors' hospital system and to explore possible causes of tool failure. This retrospective study included 8949 patients (4256 men, 4693 women; mean age, 55.

Factors Contributing to Computed Tomography Trauma Scan Times at a Tertiary Center: Improving Emergency Department Trauma Imaging Workflow Through Targeted Interventions.

Purposes: The aims of the study are to identify factors contributing to computed tomography (CT) trauma scan turnaround time variation and to evaluate the effects of an automated intervention on time metrics.

Methods: Throughput metrics were captured via picture archiving and communication system from January 1, 2018, to December 16, 2019, and included 17,709 CT trauma scans from our institution. Initial data showed that imaging technologist variation played a significant role in trauma imaging turnaround time.

Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction.

Purpose: The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT.

Methods: This retrospective study of 9066 asymptomatic adults (mean age, 57.1 ± 7.

Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool.

Purpose: To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard.

Materials And Methods: This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool.